Exploring Physiological Differences in Brain Areas Using Statistical Complexity Analysis of BOLD Signals.

The brain is a fundamental organ for the human body to function properly, for which it needs to receive a continuous flow of blood, which explains the existence of control mechanisms that act to maintain this flow as constant as possible in a process known as cerebral autoregulation. One way to obtain information on how the levels of oxygen supplied to the brain vary is through of BOLD (Magnetic Resonance) images, which have the advantage of greater spatial resolution than other forms of measurement, such as transcranial Doppler. However, they do not provide good temporal resolution nor allow for continuous prolonged examination. Thus, it is of great importance to find a method to detect regional differences from short BOLD signals. One of the existing alternatives is complexity measures that can detect changes in the variability and temporal organisation of a signal that could reflect different physiological states. The so-called statistical complexity, created to overcome the shortcomings of entropy alone to explain the concept of complexity, has shown potential with haemodynamic signals. The aim of this study is to determine by using statistical complexity whether it is possible to find differences between physiologically distinct brain areas in healthy individuals. The data set includes BOLD images of 10 people obtained at the University Hospital of Leicester NHS Trust with a 1.5 Tesla magnetic resonance imaging scanner. The data were captured for 180 s at a frequency of 1 Hz. Using various combinations of statistical complexities, no differences were found between hemispheres. However, differences were detected between grey matter and white matter, indicating that these measurements are sensitive to differences in brain tissues.

Progressive Rehabilitation Based on EMG Gesture Classification and an MPC-Driven Exoskeleton.

Stroke is a leading cause of disability and death worldwide, with a prevalence of 200 millions of cases worldwide. Motor disability is presented in 80% of patients. In this context, physical rehabilitation plays a fundamental role for gradually recovery of mobility. In this work, we designed a robotic hand exoskeleton to support rehabilitation of patients after a stroke episode. The system acquires electromyographic (EMG) signals in the forearm, and automatically estimates the movement intention for five gestures. Subsequently, we developed a predictive adaptive control of the exoskeleton to compensate for three different levels of muscle fatigue during the rehabilitation therapy exercises. The proposed system could be used to assist the rehabilitation therapy of the patients by providing a repetitive, intense, and adaptive assistance.

Hemispheric asymmetry in the processing of an emotional working memory task.

Emotion and working memory are key components in daily life experiences. Previous research has already established a connection between these processes but the neural substrates of this relationship remain an open discussion. The present study aimed to investigate the effects of the use of pictures with emotional valence on the performance of a working memory task as well as the neuronal response during the task. For this purpose, 32 participants performed a 2-back task with negative, positive, and neutral images selected from the International Affective Pictures System (IAPS). No significant difference was found in the performance or in the response time related to the valence of the images. Repeated-measures ANOVA with hemisphere and valence as factors revealed an increase of the activity in the right hemisphere for the amplitude of the ERP P3 component and for the time-locked theta power for all the images. The P3 component in the right hemisphere additionally showed greater mean amplitude for the negative images as compared to the neutral and positive ones. Together, these results suggest a predominant role of the right hemisphere for the processing of both working memory and emotional information, as well as a higher neuronal resource allocation to the processing of negative valence images which enabled a proper performance of the working memory task for the negative images.

[BCR-ABL1 negative chronic myeloid leukemia: report of two cases]. / Leucemia mieloide crónica atípica BCR-ABL1 negativa: reporte de 2 casos.

BCR-ABL1 negative atypical chronic myeloid leukemia (aCML) is a rare type of myeloproliferative / myelodysplastic syndrome characterized by leukocytosis and proliferation of dysplastic neutrophilic precursors in the absence of positivity for the BCR-ABL1 fusion gene. We report a 66-year-old woman and a 57-year-old man with aCML, who initially presented with general malaise and weight loss, associated with anemia, thrombocytopenia, and leukocytosis with left shift and dysplasia in the neutrophil series. Both evolved unfavorably after admission and died a few days later due to multiple organ failure.

Leucemia mieloide crónica atípica BCR-ABL1 negativa: reporte de 2 casos / BCR-ABL1 negative chronic myeloid leukemia: report of two cases

BCR-ABL1 negative atypical chronic myeloid leukemia (aCML) is a rare type of myeloproliferative / myelodysplastic syndrome characterized by leukocytosis and proliferation of dysplastic neutrophilic precursors in the absence of positivity for the BCR-ABL1 fusion gene. We report a 66-year-old woman and a 57-year-old man with aCML, who initially presented with general malaise and weight loss, associated with anemia, thrombocytopenia, and leukocytosis with left shift and dysplasia in the neutrophil series. Both evolved unfavorably after admission and died a few days later due to multiple organ failure.

Assist-As-Needed Exoskeleton for Hand Joint Rehabilitation Based on Muscle Effort Detection.

Robotic-assisted systems have gained significant traction in post-stroke therapies to support rehabilitation, since these systems can provide high-intensity and high-frequency treatment while allowing accurate motion-control over the patient's progress. In this paper, we tackle how to provide active support through a robotic-assisted exoskeleton by developing a novel closed-loop architecture that continually measures electromyographic signals (EMG), in order to adjust the assistance given by the exoskeleton. We used EMG signals acquired from four patients with post-stroke hand impairments for training machine learning models used to characterize muscle effort by classifying three muscular condition levels based on contraction strength, co-activation, and muscular activation measurements. The proposed closed-loop system takes into account the EMG muscle effort to modulate the exoskeleton velocity during the rehabilitation therapy. Experimental results indicate the maximum variation on velocity was 0.7 mm/s, while the proposed control system effectively modulated the movements of the exoskeleton based on the EMG readings, keeping a reference tracking error <5%.

Using complexity-entropy planes to detect Parkinson's disease from short segments of haemodynamic signals.

Objective. There is emerging evidence that analysing the entropy and complexity of biomedical signals can detect underlying changes in physiology which may be reflective of disease pathology. This approach can be used even when only short recordings of biomedical signals are available. This study aimed to determine whether entropy and complexity measures can detect differences between subjects with Parkinsons disease and healthy controls (HCs).Approach. A method based on a diagram of entropy versus complexity, named complexity-entropy plane, was used to re-analyse a dataset of cerebral haemodynamic signals from subjects with Parkinsons disease and HCs obtained under poikilocapnic conditions. A probability distribution for a set of ordinal patterns, designed to capture regularities in a time series, was computed from each signal under analysis. Four types of entropy and ten types of complexity measures were estimated from these distributions. Mean values of entropy and complexity were compared and their classification power was assessed by evaluating the best linear separator on the corresponding complexity-entropy planes.Main results. Few linear separators obtained significantly better classification, evaluated as the area under the receiver operating characteristic curve, than signal mean values. However, significant differences in both entropy and complexity were detected between the groups of participants.Significance. Measures of entropy and complexity were able to detect differences between healthy volunteers and subjects with Parkinson's disease, in poikilocapnic conditions, even though only short recordings were available for analysis. Further work is needed to refine this promising approach, and to help understand the findings in the context of specific pathophysiological changes.

Automatic seizure detection based on imaged-EEG signals through fully convolutional networks.

Seizure detection is a routine process in epilepsy units requiring manual intervention of well-trained specialists. This process could be extensive, inefficient and time-consuming, especially for long term recordings. We proposed an automatic method to detect epileptic seizures using an imaged-EEG representation of brain signals. To accomplish this, we analyzed EEG signals from two different datasets: the CHB-MIT Scalp EEG database and the EPILEPSIAE project that includes scalp and intracranial recordings. We used fully convolutional neural networks to automatically detect seizures. For our best model, we reached average accuracy and specificity values of 99.3% and 99.6%, respectively, for the CHB-MIT dataset, and corresponding values of 98.0% and 98.3% for the EPILEPSIAE patients. For these patients, the inclusion of intracranial electrodes together with scalp ones increased the average accuracy and specificity values to 99.6% and 58.3%, respectively. Regarding the other metrics, our best model reached average precision of 62.7%, recall of 58.3%, F-measure of 59.0% and AP of 54.5% on the CHB-MIT recordings, and comparatively lowers performances for the EPILEPSIAE dataset. For both databases, the number of false alarms per hour reached values less than 0.5/h for 92% of the CHB-MIT patients and less than 1.0/h for 80% of the EPILEPSIAE patients. Compared to recent studies, our lightweight approach does not need any estimation of pre-selected features and demonstrates high performances with promising possibilities for the introduction of such automatic methods in the clinical practice.

RIPPLELAB: A Comprehensive Application for the Detection, Analysis and Classification of High Frequency Oscillations in Electroencephalographic Signals.

High Frequency Oscillations (HFOs) in the brain have been associated with different physiological and pathological processes. In epilepsy, HFOs might reflect a mechanism of epileptic phenomena, serving as a biomarker of epileptogenesis and epileptogenicity. Despite the valuable information provided by HFOs, their correct identification is a challenging task. A comprehensive application, RIPPLELAB, was developed to facilitate the analysis of HFOs. RIPPLELAB provides a wide range of tools for HFOs manual and automatic detection and visual validation; all of them are accessible from an intuitive graphical user interface. Four methods for automated detection-as well as several options for visualization and validation of detected events-were implemented and integrated in the application. Analysis of multiple files and channels is possible, and new options can be added by users. All features and capabilities implemented in RIPPLELAB for automatic detection were tested through the analysis of simulated signals and intracranial EEG recordings from epileptic patients (n = 16; 3,471 analyzed hours). Visual validation was also tested, and detected events were classified into different categories. Unlike other available software packages for EEG analysis, RIPPLELAB uniquely provides the appropriate graphical and algorithmic environment for HFOs detection (visual and automatic) and validation, in such a way that the power of elaborated detection methods are available to a wide range of users (experts and non-experts) through the use of this application. We believe that this open-source tool will facilitate and promote the collaboration between clinical and research centers working on the HFOs field. The tool is available under public license and is accessible through a dedicated web site.

Efectos cardiovasculares de fingolimod en el tratamiento de la esclerosis múltiple / Cardiovascular effects of fingolimod in the treatment of multiple sclerosis

Fingolimod es un medicamento modificador de la enfermedad para pacientes con esclerosis múltiple de tipo recaída remisión; la molécula es un análogo de esfingosina fosfato que debido a su mecanismo de acción, produce en 0,5% de los pacientes, una disminución de la frecuencia cardíaca durante la administración de la primera dosis y un aumento leve de la presión arterial al segundo mes de tratamiento. En este articulo se revisan los mecanismos celulares por los cuales fingolimod causa estos eventos y se reportan recomendaciones de seguridad basados en la US Food and Drug Administration (FDA) para el inicio de pacientes en tratamiento con este medicamento.

Fingolimod is a disease modifying drug for patients with relapse remitting multiple sclerosis (RRMS). The molecule is a sphingosine phosfate analog that because of its mechanism of action causes a decrease in heart rate during the administration of the first dosis and a slight increase in blood pressure in the second month of treatment in 0.5% of patients. We review in this article the cellular mechanisms by which fingolimod causes these events and report safety recommendations based on US Food and Drug Administration (FDA) for initiating treatment with this drug.

Perfil clínico de patología neuromuscular hereditaria en el Instituto de Rehabilitación Infantil Teletón, Valparaíso, Chile / Clinical profile of patients with neuromoscular hereditary diseases in Child Rehabilitation Institute Teletón, Valparaiso, Chile

Introducción: Las enfermedades neuromusculares (ENM) son una causa importante de discapacidad progresiva en el niño. Objetivo: Describir el perfil clínico de las consultas por ENM hereditarias, atendidas actualmente en Instituto de Rehabilitación Infantil Teletón (IRI), Valparaíso. Pacientes y Método: estudio descriptivo, retrospectivo. Selección y análisis de pacientes con ENM en control activo, del registro estadístico de IRI Valparaíso. Resultados: Total 115 pacientes, hombres 70 por ciento. Edad promedio 14,9 años (rango: 1-28 a). Motivo de consulta más frecuente: trastorno de la marcha (49,5 por ciento). Las etiologías encontradas fueron: muscular (67 por ciento), neuropatías (21 por ciento) y enfermedad de motoneurona (10 por ciento). Los diagnósticos más frecuentes fueron: Distrofinopatías 30 por ciento, Charcot Marie Tooth 21,7 por ciento, Miopatías Congénitas 15,6 por ciento, Atrofia Muscular Espinal 10 por ciento, Distrofia Miotónica 7,8 por ciento. Discusión: El sexo masculino fue más prevalente lo que puede atribuirse a la mayor frecuencia de Distrofinopatías dentro de las ENM. La latencia para el diagnóstico es variable según la patología, siendo en promedio 3,2 años. Las frecuencias de diagnósticos encontrados coinciden parcialmente con la epidemiología descrita.

Introduction: Neuromuscular diseases (NMD) are a major cause of progressive disability in children. Objective: To describe the clinical profile of hereditary NMD consultations, currently being attended in IRI Valparaíso. Patients and Method: Selection and analysis of actually attending NMD patients from the IRI statistical registration. Results: 115 patients were identified, 70 percent men. Mean age 14.9 years (1-28). The most frequent cause for consultation was gait disorder (49.5 percent. Etiologies were: muscular (67 percent), neuropathy (21 percent) and motor neuron disease (10 percent). The most common diagnoses were: dystrophinopathies (30 percent), Charcot Marie Tooth 21.7 percent, Congenital Myopathy (15.6 percent), Spinal Muscular Atrophy (10 percent), Myotonic Dystrophy (7.8 percent). Discussion: Prevalence was higher for males, which is attributed to the higher frequency of dystrophinopathies. Time for diagnosis was variable depending on the disease, with a mean of 3,2 years. The frequency of NMD were partially coincidental with previously reported epidemiologic data.

Epilepsia asociada a tumores del sistema nervioso central en niños / Epilepsy associated with central nervous system tumors in children

Introducción: La prevalencia de epilepsia en pacientes con tumores del sistema nervioso central (SNC), producto del tumor per se o secundaria al tratamiento, es mayor que en la población general. El objetivo de este estudio es analizar la frecuencia y características de la epilepsia en pacientes pediátricos con tumores del SNC. Método: Estudio descriptivo retrospectivo, realizado a través de la revisión de fichas médicas de los pacientes pediátricos con tumores de SNC entre los años 2001- 2010 en Hospital Carlos Van Buren de Valparaíso. Resultados: Revisados 97 casos pediátricos de tumores del SNC, dieciocho (18,5 por ciento eran portadores de epilepsia, 2/3 sexo masculino, promedio de edad al diagnóstico del tumor fue 7 años y de primera crisis epiléptica 6 años 7 meses. Un 61 por ciento debutó con crisis epilépticas previo al diagnóstico de tumor. Dieciseis de 18 tumores (88 por ciento) fueron supratentoriales, comprometiendo principalmente el lóbulo temporal (9 de 16). Un 83 por ciento fueron neuroepiteliales, los más frecuentes fueron astrocitomas (50 por ciento). Dieciseis casos (88 por ciento) fueron sometidos a intervención quirúrgica. En relación a la epilepsia, 73 por ciento presentó crisis parciales complejas y 38 por ciento (6 casos de 16) evolucionó con epilepsia refractaria durante el seguimiento. Hubo 3 casos que fallecieron. Conclusión: Las crisis epilépticas, sobre todo las crisis focales fueron una manifestación frecuente en este grupo de pacientes pediátricos con tumores del SNC, especialmente en aquellos de localización supratentorial, ya sea como manifestación inicial y clave para el diagnóstico de tumor o durante su evolución. Un 38 por ciento evolucionó como epilepsia refractaria. El número de intervenciones quirúrgicas y la localización tumoral incidieron en la evolución de la epilepsia. Se enfatiza la importancia de una evaluación acuciosa y búsqueda etiológica, en niños que debutan con crisis epilépticas.

Introduction: Patients with brain tumors, show a higher prevalence of epilepsy than the general population, because of the tumor itself or as a consequence of treatment. The aim of this study is to analyze the incidence and characteristics of epilepsy in patients with brain tumors. Method: Retrospective descriptive study, medical records of pediatric patients with brain tumors between the years 2001-2010 from Hospital Carlos Van Buren were reviewed. Results: From 97 patients with brain tumors, 18 (18.5 percent) presented with epilepsy. Two thirds were males. Mean age for brain tumor diagnosis was 7 years, and for first epileptic seizure 6 years 7 months. In 61 percent epileptic seizures started previous to the tumor diagnosis. Sixteen out of 18 patients (88 percent) had supratentorial, mainly temporal tumors (9/16). 83 percent were neuroepithelial, from which astrocytomas were the most frequent (50 percent). Sixteen patients had surgical treatment (88 percent). Epileptic seizures were complex partial in 73 percent. 38 percent evolved to refractory epilepsy in an average of 5 year follow-up. Discussion: Epileptic seizures, mainly complex partial seizures, were a frequent manifestation of patients with brain tumors, specially supratentorial, as the initial event or in follow up. Thirty eight per cent evolved to refractory epilepsy. Number of surgical interventions and localization of the tumor affected the evolution of epileptic seizures. The relevance of searching etiology in children who have a first epileptic seizure is emphazised.